In this paper we have presented a Markov Logic Network that jointly
models all predicate identification, argument identification and
classification and sense disambiguation decisions for a sentence. We
have shown that this approach is competitive, in particular if we consider that our input parses are
significantly worse than those of the top CoNLL 2008 systems. 

We demonstrated the
benefit of jointly predicting senses and semantic arguments when
compared to a pipeline system that first picks arguments and then
senses. We also showed that by modelling whether a token is an
argument of some predicate and jointly picking arguments for all
predicates of a sentence, further improvements can be achieved.  

Finally, we demonstrated that our system is efficient, despite
following a global approach. This efficiency was also shown to stem
from the first order inference method our Markov Logic engine
applies. 

%We believe that a Markov Logic approach to Semantic Role Labelling may
%also help us to answer interesting follow-up research questions: does it help to
%enforce some type of ``subject raising'' constraints when looking at
%multiple predicates at the same time? Can we integrate additional
%stages of an actual NLP system (such as a dialogue or information
%extraction system)? Can we integrate a dependency parser?     
